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收稿日期：2023-08-17，
修回日期：2024-01-26，
纸质出版日期：2024-11-16
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目的
2
脑室是人脑重要结构，在临床实践中，其大小、形状变化与多种慢性和急性神经系统疾病息息相关，对脑室的精确分割能够为脑部相关疾病的诊断提供有价值的辅助信息。随着深度学习在医学图像处理领域的迅速发展，医学图像分割任务取得了重大进展。然而，脑室内出血患者的脑室分割问题仍然有待探索。
方法
2
本文聚焦于脑室内出血患者的脑室分割问题，针对其面临的目标遮挡、边界不清晰等问题，提出针对性的脑室分割算法——基于端到端对比学习对称感知的脑室分割网络。该模型首先基于空间转换网络实现自适应图像校正，获取任意角度下输入图像的脑室对称图像。然后通过对比学习算法并结合加权对称损失函数施加对图像的对称性约束。通过上述方法可实现脑室分割网络的端到端训练，上游网络与下游分割任务协同合作。
结果
2
基于不同分割网络模型的实验结果表明，该方案在脑室内出血患者的脑室分割任务上可取得性能提升，该方案按病例和切片评估的Dice系数指标平均增益分别达到1.09%和1.28%。结合本文算法，最优模型按病例评估的DSC（Dice similarity coefficient）系数和召回率分别达到85.17%和84.03%。
结论
2
本文所提出算法对CT（computed tomography）和MR（magnetic resonance）图像的脑室分割均取得了有效提升，对脑室内出血患者相关医学图像分割提升尤为显著，并且本文方法可移植性强，可适用于多种分割网络。
Objective
2
Cerebral ventricles are one of the most prominent cerebral structures. The size and shape changes of the cerebral ventricle are closely associated with diverse acute and chronic neurological diseases. Accurate ventricle segmentation can help diagnose brain-related diseases by providing valuable auxiliary information. However， manual delineation of cerebral ventricles is a time-consuming task； thus， automatic ventricle segmentation is necessary. Fortunately， with the rapid development of deep learning in the field of medical image processing， automatic medical image segmentation has made considerable progress. However， the ventricle segmentation in patients with intraventricular hemorrhage （IVH） remains unexplored. A few studies focus on the ventricle segmentation of patients with IVH.
Method
2
Cerebral ventricle segmentation can be categorized into healthy/normal and IVH cases. Cerebral ventricles in healthy/normal cases are characterized by their high contrast and clear boundaries. The main challenge lies in the segmentation of small-scale cerebral ventricles in some slices. Notably， in healthy/normal cases， cerebral ventricles are not perfectly symmetric； therefore， penalizing a symmetry constraint would be helpful， especially in dealing with the low-contrast small-scale regions. Cerebral ventricle segmentation in healthy/normal cases is generally less challenging. According to the sizes of IVH， the IVH cases are further classified into small- and large-scale IVH cases. For the small-scale IVH cases， though parts of the cerebral ventricles are completely filled by hemorrhages， only the boundary regions would be affected during segmentation. In these cases， the IVH problem would not significantly degrade the segmentation performance because those regions （i.e.， cerebral ventricles filled by IVH） are of high contrast compared to the background， and segmenting the high-contrast regions is roughly equal to cerebral ventricle segmentation. Large-scale IVH cases are the most challenging problem in cerebral ventricle segmentation. Considering the large hemorrhages， the hemorrhages not only cover parts of the cerebral ventricles but also several background regions. All the regions share similar appearances and contrasts. Classifying these regions as background would produce numerous false negatives， while segmenting them as cerebral ventricles would generate quite a few false positives. Therefore， large-scale IVH would poorly affect cerebral ventricle segmentation performance. Based on the above analysis， this study focuses on the cerebral ventricle segmentation problems of patients with IVH and proposes targeted ventricle segmentation methods for the problems of target occlusion and unclear boundaries. The core idea of the proposed framework is to utilize the symmetry of cerebral ventricles as guidance to alleviate the occlusions formed by IVH. Thus， an end-to-end contrastive learning-based symmetry-aware ventricle segmentation network is proposed in the paper. The model first implements adaptive image correction based on spatial transform networks without additional annotations to obtain the ventricle symmetric images of the input images at any position. A symmetry-aware learning loop is then constructed. The symmetric image pairs are simultaneously inputted into the segmentation network. The ventricles predicted by the network are forced to be symmetric by emphasizing the similarity of the segmentation result pairs. Thus， the occlusions formed by IVH could be alleviated by referring to the healthy ventricles. The ventricles are not completely symmetric； thus， pursuing “hard” symmetry during training is infeasible. Therefore， the contrastive learning algorithm is further combined with the weighted symmetry loss function to impose symmetry constraints on the images. The network can be trained end-to-end， enabling the upstream network to collaborate with the downstream segmentation task.
Result
2
Experimental results based on different segmentation network models demonstrate that the approach proposed in this paper can achieve consistent performance improvements in multiple evaluation metrics in the ventricle segmentation task of patients with IVH. The average increase in patient- and slice-wise dice coefficients based on different baseline models when introducing the proposed method is 1.09% and 1.28%， respectively. When evaluated on the patient level， the optimal model achieved a Dice coefficient and recall of 85.17% and 84.03%， respectively， by incorporating the algorithm proposed in this paper. The qualitative results also reveal the superior performance of the proposed algorithm， which achieves smooth boundaries and complete ventricles with fewer false positives.
Conclusion
2
This paper focuses on cerebral ventricle segmentation， especially with the existence of IVH. Compared to cerebral ventricle segmentation in healthy/normal cases， occlusions formed by IVH would make it challenging for segmentation. Based on the symmetry of cerebral ventricles， a symmetry-aware approach combined with contrastive self-supervised learning is introduced. Therefore， the occlusions are effectively alleviated by referring to the healthy/normal parts of the cerebral ventricles. Experimental results on two different datasets demonstrate a notable advancement in ventricle segmentation of computed tomography（CT） and magnetic resonance（MR） images of healthy/normal and IVH cases. Moreover， the IVH cases demonstrate considerable improvement. More importantly， the proposed approach is independent of specific deep learning architectures and introduces no additional computational complexity. Therefore， the method presented in this paper has strong portability and can be applied to various segmentation networks.
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